code: def act(self, observation, reward, done):
      self.step += 1
      offset = 0
      self.sub_topo_dict = {}
      for sub_id, sub_elem_num in enumerate(observation.sub_info):
          sub_topo = observation.topo_vect[
              offset : offset + sub_elem_num
          ]
          offset += sub_elem_num
          self.sub_topo_dict[sub_id] = sub_topo
  
      disconnected = np.where(observation.line_status == False)[
          0
      ].tolist()
      to_maintain_lines = np.where(
          (observation.time_next_maintenance > 0)
          & (observation.time_next_maintenance < 15)
      )[0]
      to_maintain_lines = to_maintain_lines.tolist()
      if len(disconnected) > 0:
          for line_id in disconnected:
              if (
                  observation.time_before_cooldown_line[line_id] == 0
                  and line_id not in to_maintain_lines
              ):
                  reset_action = self.action_space(
                      {"set_line_status": [(line_id, +1)]}
                  )
                  (
                      obs_simulate,
                      reward_simulate,
                      done_simulate,
                      info_simulate,
                  ) = observation.simulate(reset_action)
                  observation._obs_env._reset_to_orig_state()
                  if (
                      np.max(observation.rho) < 1.0
                      and np.max(obs_simulate.rho) >= 1.0
                  ):
                      continue
                  combined_action, sub_id = self.avoid_overflow(
                      observation, reset_action
                  )
                  return combined_action
  
      if observation is not None and not any(np.isnan(observation.rho)):
          if np.max(observation.rho) < 0.94 and np.any(
              observation.topo_vect == 2
          ):
              offset = 0
              for sub_id, sub_elem_num in enumerate(
                  observation.sub_info
              ):
                  sub_topo = self.sub_topo_dict[sub_id]
  
                  if (
                      np.any(sub_topo == 2)
                      and observation.time_before_cooldown_sub[sub_id]
                      == 0
                  ):
                      sub_topo = np.where(
                          sub_topo == 2, 1, sub_topo
                      )  # bus 2 to bus 1
                      sub_topo = np.where(
                          sub_topo == -1, 0, sub_topo
                      )  # don't do action in bus=-1
                      reconfig_sub = self.action_space(
                          {
                              "set_bus": {
                                  "substations_id": [(sub_id, sub_topo)]
                              }
                          }
                      )
  
                      (
                          obs_simulate,
                          reward_simulate,
                          done_simulate,
                          info_simulate,
                      ) = observation.simulate(reconfig_sub)
                      observation._obs_env._reset_to_orig_state()
                      assert (
                          not info_simulate["is_illegal"]
                          and not info_simulate["is_ambiguous"]
                      )
  
                      if (
                          not done_simulate
                          and obs_simulate is not None
                          and not any(np.isnan(obs_simulate.rho))
                      ):
                          if np.max(obs_simulate.rho) < 0.95:
                              return reconfig_sub
                          else:
                              pass
      action, sub_id = self.avoid_overflow(observation)
      return action
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/rl_agent.py
imports: import os
  import numpy
  from copy import deepcopy
  from utils import process
  from powernet_model import PowerNetModel
  from grid2op.Agent import BaseAgent
  import torch
label_desc: Use image node_id 9 for calling the RLAgent obj's underlying member method code with example usage: obj.act(observation, reward, done) and returns: action, combined_action, reconfig_sub